Overview

Dataset statistics

Number of variables15
Number of observations5701
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory668.2 KiB
Average record size in memory120.0 B

Variable types

Numeric15

Alerts

gross_revenue is highly correlated with qnt_purchases and 3 other fieldsHigh correlation
recency_days is highly correlated with qnt_purchasesHigh correlation
qnt_purchases is highly correlated with gross_revenue and 4 other fieldsHigh correlation
tot_stock_code is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qnt_items is highly correlated with gross_revenue and 4 other fieldsHigh correlation
freq_purchase is highly correlated with qnt_purchases and 1 other fieldsHigh correlation
qtd_returned is highly correlated with qnt_purchases and 3 other fieldsHigh correlation
freq_returns is highly correlated with qtd_returned and 2 other fieldsHigh correlation
avg_basket_size is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_basket_variety is highly correlated with tot_stock_code and 1 other fieldsHigh correlation
item_rp_ratio is highly correlated with qtd_returned and 2 other fieldsHigh correlation
net_margin is highly correlated with qtd_returned and 2 other fieldsHigh correlation
gross_revenue is highly correlated with qnt_purchases and 1 other fieldsHigh correlation
qnt_purchases is highly correlated with gross_revenue and 1 other fieldsHigh correlation
tot_stock_code is highly correlated with avg_basket_varietyHigh correlation
qnt_items is highly correlated with gross_revenue and 1 other fieldsHigh correlation
avg_basket_variety is highly correlated with tot_stock_codeHigh correlation
item_rp_ratio is highly correlated with net_marginHigh correlation
net_margin is highly correlated with item_rp_ratioHigh correlation
gross_revenue is highly correlated with qnt_purchases and 3 other fieldsHigh correlation
qnt_purchases is highly correlated with gross_revenue and 2 other fieldsHigh correlation
tot_stock_code is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qnt_items is highly correlated with gross_revenue and 3 other fieldsHigh correlation
freq_purchase is highly correlated with qnt_purchasesHigh correlation
qtd_returned is highly correlated with freq_returns and 2 other fieldsHigh correlation
freq_returns is highly correlated with qtd_returned and 2 other fieldsHigh correlation
avg_basket_size is highly correlated with gross_revenue and 1 other fieldsHigh correlation
avg_basket_variety is highly correlated with tot_stock_codeHigh correlation
item_rp_ratio is highly correlated with qtd_returned and 2 other fieldsHigh correlation
net_margin is highly correlated with qtd_returned and 2 other fieldsHigh correlation
df_index is highly correlated with customer_id and 1 other fieldsHigh correlation
customer_id is highly correlated with df_index and 1 other fieldsHigh correlation
gross_revenue is highly correlated with qnt_purchases and 3 other fieldsHigh correlation
recency_days is highly correlated with df_index and 1 other fieldsHigh correlation
qnt_purchases is highly correlated with gross_revenue and 3 other fieldsHigh correlation
tot_stock_code is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qnt_items is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly correlated with qtd_returned and 2 other fieldsHigh correlation
qtd_returned is highly correlated with gross_revenue and 4 other fieldsHigh correlation
avg_basket_size is highly correlated with avg_ticket and 2 other fieldsHigh correlation
avg_basket_variety is highly correlated with tot_stock_code and 1 other fieldsHigh correlation
item_rp_ratio is highly correlated with net_marginHigh correlation
net_margin is highly correlated with avg_ticket and 1 other fieldsHigh correlation
gross_revenue is highly skewed (γ1 = 23.02670803) Skewed
qnt_items is highly skewed (γ1 = 25.11648179) Skewed
avg_ticket is highly skewed (γ1 = 48.21449998) Skewed
qtd_returned is highly skewed (γ1 = 30.50367005) Skewed
df_index is uniformly distributed Uniform
df_index has unique values Unique
customer_id has unique values Unique
qtd_returned has 4200 (73.7%) zeros Zeros
freq_returns has 4200 (73.7%) zeros Zeros
item_rp_ratio has 4200 (73.7%) zeros Zeros

Reproduction

Analysis started2021-10-22 23:30:08.748377
Analysis finished2021-10-22 23:30:29.991268
Duration21.24 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct5701
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2898.856692
Minimum0
Maximum5791
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T20:30:30.285358image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile290
Q11456
median2901
Q34345
95-th percentile5500
Maximum5791
Range5791
Interquartile range (IQR)2889

Descriptive statistics

Standard deviation1670.639329
Coefficient of variation (CV)0.576309734
Kurtosis-1.196215647
Mean2898.856692
Median Absolute Deviation (MAD)1445
Skewness-0.003585702485
Sum16526382
Variance2791035.767
MonotonicityStrictly increasing
2021-10-22T20:30:30.386084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
38501
 
< 0.1%
38701
 
< 0.1%
38691
 
< 0.1%
38681
 
< 0.1%
38671
 
< 0.1%
38661
 
< 0.1%
38651
 
< 0.1%
38641
 
< 0.1%
38631
 
< 0.1%
Other values (5691)5691
99.8%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
57911
< 0.1%
57901
< 0.1%
57891
< 0.1%
57881
< 0.1%
57871
< 0.1%
57861
< 0.1%
57851
< 0.1%
57841
< 0.1%
57831
< 0.1%
57821
< 0.1%

customer_id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct5701
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16603.65006
Minimum12347
Maximum22709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T20:30:30.484604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12700
Q114288
median16229
Q318215
95-th percentile21747
Maximum22709
Range10362
Interquartile range (IQR)3927

Descriptive statistics

Standard deviation2811.544204
Coefficient of variation (CV)0.1693328993
Kurtosis-0.8242992836
Mean16603.65006
Median Absolute Deviation (MAD)1964
Skewness0.4409376168
Sum94657409
Variance7904780.81
MonotonicityNot monotonic
2021-10-22T20:30:30.583320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
134611
 
< 0.1%
210921
 
< 0.1%
210911
 
< 0.1%
171231
 
< 0.1%
178911
 
< 0.1%
164981
 
< 0.1%
137451
 
< 0.1%
155841
 
< 0.1%
210891
 
< 0.1%
Other values (5691)5691
99.8%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123491
< 0.1%
123501
< 0.1%
123521
< 0.1%
123531
< 0.1%
123541
< 0.1%
123551
< 0.1%
123561
< 0.1%
123571
< 0.1%
ValueCountFrequency (%)
227091
< 0.1%
227081
< 0.1%
227071
< 0.1%
227061
< 0.1%
227051
< 0.1%
227041
< 0.1%
227001
< 0.1%
226991
< 0.1%
226961
< 0.1%
226951
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct5457
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1759.775187
Minimum0.42
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T20:30:30.677752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile13.3
Q1237.07
median612.78
Q31568.23
95-th percentile5298.48
Maximum279138.02
Range279137.6
Interquartile range (IQR)1331.16

Descriptive statistics

Standard deviation7512.185767
Coefficient of variation (CV)4.268832646
Kurtosis698.9271054
Mean1759.775187
Median Absolute Deviation (MAD)478.3
Skewness23.02670803
Sum10032478.34
Variance56432935
MonotonicityNot monotonic
2021-10-22T20:30:30.765217image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.959
 
0.2%
1.258
 
0.1%
2.958
 
0.1%
4.958
 
0.1%
12.757
 
0.1%
1.657
 
0.1%
3.757
 
0.1%
7.56
 
0.1%
5.956
 
0.1%
4.256
 
0.1%
Other values (5447)5629
98.7%
ValueCountFrequency (%)
0.421
 
< 0.1%
0.651
 
< 0.1%
0.791
 
< 0.1%
0.844
0.1%
0.853
 
0.1%
1.071
 
< 0.1%
1.258
0.1%
1.441
 
< 0.1%
1.657
0.1%
1.691
 
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
140450.721
< 0.1%
124564.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%
65039.621
< 0.1%

recency_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct304
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.7938958
Minimum0
Maximum373
Zeros37
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T20:30:30.857735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q123
median71
Q3199
95-th percentile338
Maximum373
Range373
Interquartile range (IQR)176

Descriptive statistics

Standard deviation111.4937421
Coefficient of variation (CV)0.9546196002
Kurtosis-0.6361474179
Mean116.7938958
Median Absolute Deviation (MAD)61
Skewness0.8161795168
Sum665842
Variance12430.85453
MonotonicityNot monotonic
2021-10-22T20:30:30.950936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1110
 
1.9%
4105
 
1.8%
398
 
1.7%
292
 
1.6%
1086
 
1.5%
882
 
1.4%
1779
 
1.4%
979
 
1.4%
778
 
1.4%
1567
 
1.2%
Other values (294)4825
84.6%
ValueCountFrequency (%)
037
 
0.6%
1110
1.9%
292
1.6%
398
1.7%
4105
1.8%
552
0.9%
778
1.4%
882
1.4%
979
1.4%
1086
1.5%
ValueCountFrequency (%)
37323
0.4%
37222
0.4%
37117
0.3%
3694
 
0.1%
36813
0.2%
36716
0.3%
36615
0.3%
36519
0.3%
36411
0.2%
3627
 
0.1%

qnt_purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.469566743
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T20:30:31.049204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile11
Maximum206
Range205
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.80995105
Coefficient of variation (CV)1.962766984
Kurtosis302.3815489
Mean3.469566743
Median Absolute Deviation (MAD)0
Skewness13.19904821
Sum19780
Variance46.3754333
MonotonicityNot monotonic
2021-10-22T20:30:31.138788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12873
50.4%
2828
 
14.5%
3504
 
8.8%
4394
 
6.9%
5237
 
4.2%
6173
 
3.0%
7138
 
2.4%
898
 
1.7%
969
 
1.2%
1055
 
1.0%
Other values (46)332
 
5.8%
ValueCountFrequency (%)
12873
50.4%
2828
 
14.5%
3504
 
8.8%
4394
 
6.9%
5237
 
4.2%
6173
 
3.0%
7138
 
2.4%
898
 
1.7%
969
 
1.2%
1055
 
1.0%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
< 0.1%
861
< 0.1%
721
< 0.1%
622
< 0.1%
601
< 0.1%
571
< 0.1%

tot_stock_code
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct439
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.67251359
Minimum1
Maximum1786
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T20:30:31.229411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q113
median36
Q384
95-th percentile241
Maximum1786
Range1785
Interquartile range (IQR)71

Descriptive statistics

Standard deviation101.6752222
Coefficient of variation (CV)1.459330472
Kurtosis43.93242609
Mean69.67251359
Median Absolute Deviation (MAD)28
Skewness4.706142862
Sum397203
Variance10337.8508
MonotonicityNot monotonic
2021-10-22T20:30:31.321718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1277
 
4.9%
2149
 
2.6%
3112
 
2.0%
10101
 
1.8%
597
 
1.7%
996
 
1.7%
693
 
1.6%
893
 
1.6%
1193
 
1.6%
790
 
1.6%
Other values (429)4500
78.9%
ValueCountFrequency (%)
1277
4.9%
2149
2.6%
3112
2.0%
490
 
1.6%
597
 
1.7%
693
 
1.6%
790
 
1.6%
893
 
1.6%
996
 
1.7%
10101
 
1.8%
ValueCountFrequency (%)
17861
< 0.1%
17661
< 0.1%
13221
< 0.1%
11181
< 0.1%
11091
< 0.1%
8841
< 0.1%
8171
< 0.1%
7481
< 0.1%
7301
< 0.1%
7201
< 0.1%

qnt_items
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1838
Distinct (%)32.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean950.9510612
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T20:30:31.419153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q1106
median317
Q3804
95-th percentile2924
Maximum196844
Range196843
Interquartile range (IQR)698

Descriptive statistics

Standard deviation4187.017047
Coefficient of variation (CV)4.402978469
Kurtosis943.8119157
Mean950.9510612
Median Absolute Deviation (MAD)253
Skewness25.11648179
Sum5421372
Variance17531111.76
MonotonicityNot monotonic
2021-10-22T20:30:31.514400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1113
 
2.0%
273
 
1.3%
351
 
0.9%
449
 
0.9%
535
 
0.6%
629
 
0.5%
1225
 
0.4%
8822
 
0.4%
7221
 
0.4%
720
 
0.4%
Other values (1828)5263
92.3%
ValueCountFrequency (%)
1113
2.0%
273
1.3%
351
0.9%
449
0.9%
535
 
0.6%
629
 
0.5%
720
 
0.4%
818
 
0.3%
97
 
0.1%
1017
 
0.3%
ValueCountFrequency (%)
1968441
< 0.1%
802631
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
633121
< 0.1%
583431
< 0.1%
578851
< 0.1%
502551
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct5508
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.17621264
Minimum0.42
Maximum13305.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T20:30:31.610710image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile3.461111111
Q17.95
median15.85454545
Q321.96956522
95-th percentile75.92857143
Maximum13305.5
Range13305.08
Interquartile range (IQR)14.01956522

Descriptive statistics

Standard deviation210.3688589
Coefficient of variation (CV)6.74773621
Kurtosis2853.27024
Mean31.17621264
Median Absolute Deviation (MAD)7.491045455
Skewness48.21449998
Sum177735.5883
Variance44255.05681
MonotonicityNot monotonic
2021-10-22T20:30:31.707184image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.7511
 
0.2%
4.9510
 
0.2%
2.959
 
0.2%
1.259
 
0.2%
7.958
 
0.1%
8.257
 
0.1%
12.757
 
0.1%
1.657
 
0.1%
5.956
 
0.1%
4.156
 
0.1%
Other values (5498)5621
98.6%
ValueCountFrequency (%)
0.423
0.1%
0.5351
 
< 0.1%
0.651
 
< 0.1%
0.791
 
< 0.1%
0.83714285711
 
< 0.1%
0.842
< 0.1%
0.853
0.1%
1.0022222221
 
< 0.1%
1.021
 
< 0.1%
1.038751
 
< 0.1%
ValueCountFrequency (%)
13305.51
< 0.1%
4453.431
< 0.1%
38611
< 0.1%
3202.921
< 0.1%
30961
< 0.1%
1687.21
< 0.1%
1377.0777781
< 0.1%
1001.21
< 0.1%
952.98751
< 0.1%
931.51
< 0.1%

freq_purchase
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1226
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.547443855
Minimum0.005449591281
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T20:30:31.801415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.01104972376
Q10.025
median1
Q31
95-th percentile1
Maximum17
Range16.99455041
Interquartile range (IQR)0.975

Descriptive statistics

Standard deviation0.550492169
Coefficient of variation (CV)1.005568268
Kurtosis138.7489721
Mean0.547443855
Median Absolute Deviation (MAD)0
Skewness4.849528892
Sum3120.977417
Variance0.3030416281
MonotonicityNot monotonic
2021-10-22T20:30:31.894803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12881
50.5%
248
 
0.8%
0.062518
 
0.3%
0.0277777777817
 
0.3%
0.0238095238116
 
0.3%
0.0833333333315
 
0.3%
0.0344827586215
 
0.3%
0.0909090909115
 
0.3%
0.0294117647114
 
0.2%
0.0357142857113
 
0.2%
Other values (1216)2649
46.5%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
< 0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
< 0.1%
0.005665722381
 
< 0.1%
0.0056818181822
< 0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
171
 
< 0.1%
41
 
< 0.1%
35
 
0.1%
248
 
0.8%
1.1428571431
 
< 0.1%
12881
50.5%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.5508021391
 
< 0.1%
0.53351206431
 
< 0.1%

qtd_returned
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct211
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.98561656
Minimum0
Maximum9014
Zeros4200
Zeros (%)73.7%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T20:30:31.992934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile38
Maximum9014
Range9014
Interquartile range (IQR)1

Descriptive statistics

Standard deviation204.483973
Coefficient of variation (CV)11.36930571
Kurtosis1147.978744
Mean17.98561656
Median Absolute Deviation (MAD)0
Skewness30.50367005
Sum102536
Variance41813.69523
MonotonicityNot monotonic
2021-10-22T20:30:32.083040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04200
73.7%
1169
 
3.0%
2151
 
2.6%
3105
 
1.8%
489
 
1.6%
678
 
1.4%
561
 
1.1%
1252
 
0.9%
744
 
0.8%
843
 
0.8%
Other values (201)709
 
12.4%
ValueCountFrequency (%)
04200
73.7%
1169
 
3.0%
2151
 
2.6%
3105
 
1.8%
489
 
1.6%
561
 
1.1%
678
 
1.4%
744
 
0.8%
843
 
0.8%
941
 
0.7%
ValueCountFrequency (%)
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%
17761
< 0.1%
15941
< 0.1%

freq_returns
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct428
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1617817603
Minimum0
Maximum4
Zeros4200
Zeros (%)73.7%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T20:30:32.176508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.009950248756
95-th percentile1
Maximum4
Range4
Interquartile range (IQR)0.009950248756

Descriptive statistics

Standard deviation0.3731179676
Coefficient of variation (CV)2.306304288
Kurtosis4.854269275
Mean0.1617817603
Median Absolute Deviation (MAD)0
Skewness2.200043372
Sum922.3178154
Variance0.1392170177
MonotonicityNot monotonic
2021-10-22T20:30:32.291533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04200
73.7%
1854
 
15.0%
214
 
0.2%
0.58
 
0.1%
0.28571428578
 
0.1%
0.025641025647
 
0.1%
0.256
 
0.1%
0.0094786729865
 
0.1%
0.019607843145
 
0.1%
0.012987012995
 
0.1%
Other values (418)589
 
10.3%
ValueCountFrequency (%)
04200
73.7%
0.0055710306411
 
< 0.1%
0.0056818181822
 
< 0.1%
0.0058651026391
 
< 0.1%
0.0059347181011
 
< 0.1%
0.0059523809521
 
< 0.1%
0.0060240963861
 
< 0.1%
0.0060422960731
 
< 0.1%
0.0061728395061
 
< 0.1%
0.0061919504641
 
< 0.1%
ValueCountFrequency (%)
41
 
< 0.1%
31
 
< 0.1%
214
 
0.2%
1854
15.0%
0.751
 
< 0.1%
0.66666666674
 
0.1%
0.58
 
0.1%
0.42857142861
 
< 0.1%
0.44
 
0.1%
0.33333333331
 
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2368
Distinct (%)41.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean248.2129566
Minimum1
Maximum14149
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T20:30:32.400681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q175
median152
Q3290.625
95-th percentile732
Maximum14149
Range14148
Interquartile range (IQR)215.625

Descriptive statistics

Standard deviation439.2130469
Coefficient of variation (CV)1.769500887
Kurtosis379.1188261
Mean248.2129566
Median Absolute Deviation (MAD)96.5
Skewness14.58135125
Sum1415062.066
Variance192908.1005
MonotonicityNot monotonic
2021-10-22T20:30:32.504805image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1114
 
2.0%
272
 
1.3%
351
 
0.9%
449
 
0.9%
535
 
0.6%
629
 
0.5%
1226
 
0.5%
10022
 
0.4%
7222
 
0.4%
7321
 
0.4%
Other values (2358)5260
92.3%
ValueCountFrequency (%)
1114
2.0%
272
1.3%
351
0.9%
3.3333333331
 
< 0.1%
449
0.9%
535
 
0.6%
5.3333333331
 
< 0.1%
5.6666666671
 
< 0.1%
629
 
0.5%
6.1428571431
 
< 0.1%
ValueCountFrequency (%)
141491
< 0.1%
139561
< 0.1%
78241
< 0.1%
6009.3333331
< 0.1%
59631
< 0.1%
51971
< 0.1%
43001
< 0.1%
42821
< 0.1%
42801
< 0.1%
41361
< 0.1%

avg_basket_variety
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1171
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.26967522
Minimum0.2
Maximum1109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T20:30:32.605164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile1
Q17.285714286
median15.11111111
Q331
95-th percentile173
Maximum1109
Range1108.8
Interquartile range (IQR)23.71428571

Descriptive statistics

Standard deviation76.84283226
Coefficient of variation (CV)2.061805792
Kurtosis32.92217973
Mean37.26967522
Median Absolute Deviation (MAD)9.888888889
Skewness5.075661182
Sum212474.4185
Variance5904.82087
MonotonicityNot monotonic
2021-10-22T20:30:32.698290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1276
 
4.8%
2161
 
2.8%
3115
 
2.0%
9105
 
1.8%
10105
 
1.8%
8103
 
1.8%
7101
 
1.8%
6101
 
1.8%
5100
 
1.8%
1397
 
1.7%
Other values (1161)4437
77.8%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.253
 
0.1%
0.33333333337
0.1%
0.41
 
< 0.1%
0.40909090911
 
< 0.1%
0.512
0.2%
0.54545454551
 
< 0.1%
0.55555555561
 
< 0.1%
0.57142857141
 
< 0.1%
0.61764705881
 
< 0.1%
ValueCountFrequency (%)
11091
< 0.1%
7481
< 0.1%
7301
< 0.1%
7201
< 0.1%
7031
< 0.1%
6861
< 0.1%
6751
< 0.1%
6731
< 0.1%
6601
< 0.1%
6491
< 0.1%

item_rp_ratio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1379
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01210009213
Minimum0
Maximum1
Zeros4200
Zeros (%)73.7%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T20:30:32.794367image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.001038421599
95-th percentile0.04109589041
Maximum1
Range1
Interquartile range (IQR)0.001038421599

Descriptive statistics

Standard deviation0.06503737724
Coefficient of variation (CV)5.37494893
Kurtosis132.4831683
Mean0.01210009213
Median Absolute Deviation (MAD)0
Skewness10.46142317
Sum68.98262522
Variance0.004229860438
MonotonicityNot monotonic
2021-10-22T20:30:32.890229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04200
73.7%
111
 
0.2%
0.010752688174
 
0.1%
0.0096618357494
 
0.1%
0.032258064523
 
0.1%
0.0092592592593
 
0.1%
0.038461538463
 
0.1%
0.02439024393
 
0.1%
0.023809523813
 
0.1%
0.0074626865673
 
0.1%
Other values (1369)1464
 
25.7%
ValueCountFrequency (%)
04200
73.7%
0.00011696362431
 
< 0.1%
0.00018399264031
 
< 0.1%
0.00028169014081
 
< 0.1%
0.00031407035181
 
< 0.1%
0.00036192544341
 
< 0.1%
0.00036324010171
 
< 0.1%
0.00036376864311
 
< 0.1%
0.00036710719531
 
< 0.1%
0.0003930817611
 
< 0.1%
ValueCountFrequency (%)
111
0.2%
0.98630136991
 
< 0.1%
0.83333333331
 
< 0.1%
0.63333333331
 
< 0.1%
0.61151079141
 
< 0.1%
0.60088365241
 
< 0.1%
0.59645669291
 
< 0.1%
0.56488549621
 
< 0.1%
0.56463878331
 
< 0.1%
0.56020408161
 
< 0.1%

net_margin
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1495
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9849027449
Minimum0
Maximum1
Zeros11
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T20:30:32.985797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.9308852276
Q10.9979149666
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.002085033385

Descriptive statistics

Standard deviation0.06747936676
Coefficient of variation (CV)0.06851373611
Kurtosis117.1473911
Mean0.9849027449
Median Absolute Deviation (MAD)0
Skewness-9.667344946
Sum5614.930549
Variance0.004553464938
MonotonicityNot monotonic
2021-10-22T20:30:33.092341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13722
65.3%
1224
 
3.9%
1161
 
2.8%
193
 
1.6%
011
 
0.2%
0.98097273151
 
< 0.1%
0.97919265731
 
< 0.1%
0.97072125231
 
< 0.1%
0.99476653251
 
< 0.1%
0.96312878591
 
< 0.1%
Other values (1485)1485
 
26.0%
ValueCountFrequency (%)
011
0.2%
0.049466537341
 
< 0.1%
0.13368983961
 
< 0.1%
0.14017054081
 
< 0.1%
0.25023409121
 
< 0.1%
0.28295102291
 
< 0.1%
0.3241179911
 
< 0.1%
0.35486018641
 
< 0.1%
0.481
 
< 0.1%
0.4835800721
 
< 0.1%
ValueCountFrequency (%)
1224
 
3.9%
13722
65.3%
1161
 
2.8%
193
 
1.6%
0.99991807691
 
< 0.1%
0.99984316131
 
< 0.1%
0.99972431321
 
< 0.1%
0.99969169041
 
< 0.1%
0.99967287611
 
< 0.1%
0.99961497151
 
< 0.1%

Interactions

2021-10-22T20:30:28.418343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:09.777653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:11.069101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:12.416585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:13.693497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:15.101635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:16.331151image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:17.768792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:19.121517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:20.535456image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:21.854147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:23.085630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:24.404995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:25.898839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:27.170981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:28.500092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:09.882717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:11.151561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:12.502215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:13.778910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:15.183675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:16.418279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:17.860227image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:19.207249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:20.622814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:21.936731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:23.174863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:24.491574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:25.982371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:27.254188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:28.581130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:09.969883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:11.232021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:12.587463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:13.968122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:15.264368image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:16.508643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:17.948525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:19.287726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:20.707535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:22.016242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:23.260596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:24.575334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:26.065252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:27.335805image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:28.662493image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:10.053281image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:11.313895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:12.673077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:14.053744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:15.343589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:16.594265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:18.037498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:19.368892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:20.790884image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:22.095981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:23.346743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:24.843383image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:26.147727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:27.417672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:28.748890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:10.140467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:11.399233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:12.758516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:14.144904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:15.428303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:16.685572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:18.129120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:19.454768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:20.879330image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:22.180916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:23.436110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:24.932263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:26.232614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:27.503248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:28.829494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:10.223667image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:11.561858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:12.836194image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:14.225222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:15.505057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T20:30:18.218414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:19.534283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:20.960340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T20:30:29.083709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T20:30:13.090629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:14.486664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T20:30:10.647750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T20:30:13.263043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:14.670172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T20:30:28.087495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T20:30:10.823601image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T20:30:12.169332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T20:30:25.728062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T20:30:28.338816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-10-22T20:30:33.187744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-22T20:30:33.327725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-22T20:30:33.467892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-22T20:30:33.607844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-22T20:30:29.741630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-22T20:30:29.925895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysqnt_purchasestot_stock_codeqnt_itemsavg_ticketfreq_purchaseqtd_returnedfreq_returnsavg_basket_sizeavg_basket_varietyitem_rp_rationet_margin
00178505391.21372.034.021.01733.018.15222217.00000040.01.00000050.9705880.6176470.0230810.980973
11130473232.5956.09.0105.01390.018.9040350.02830235.00.023973154.44444411.6666670.0251800.955611
22125836705.382.015.0114.05028.028.9025000.04032350.00.105263335.2000007.6000000.0099440.988660
3313748948.2595.05.024.0439.033.8660710.0179210.00.00000087.8000004.8000000.0000001.000000
4415100876.00333.03.01.080.0292.0000000.07317122.00.07894726.6666670.3333330.2750000.725000
55152914623.3025.014.061.02102.045.3264710.04011529.00.032468150.1428574.3571430.0137960.984472
66146885630.877.021.0148.03621.017.2197860.057221399.00.019608172.4285717.0476190.1101910.907032
77178095411.9116.012.046.02057.088.7198360.03352041.00.013072171.4166673.8333330.0199320.987609
881531160767.900.091.0567.038194.025.5434640.243316474.00.072193419.7142866.2307690.0124100.977808
99160982005.6387.07.034.0613.029.9347760.0243900.00.00000087.5714294.8571430.0000001.000000

Last rows

df_indexcustomer_idgross_revenuerecency_daysqnt_purchasestot_stock_codeqnt_itemsavg_ticketfreq_purchaseqtd_returnedfreq_returnsavg_basket_sizeavg_basket_varietyitem_rp_rationet_margin
56915782227004839.421.01.055.01074.078.0551611.00.00.01074.055.00.01.0
5692578313298360.001.01.02.096.0180.0000001.00.00.096.02.00.01.0
5693578414569227.391.01.010.079.018.9491671.00.00.079.010.00.01.0
569457852270417.901.01.07.014.02.5571431.00.00.014.07.00.01.0
56955786227053.351.01.02.02.01.6750001.00.00.02.02.00.01.0
56965787227065699.001.01.0634.01747.08.9889591.00.00.01747.0634.00.01.0
56975788227076756.060.01.0730.02010.09.2548771.00.00.02010.0730.00.01.0
56985789227083217.200.01.056.0654.054.5288141.00.00.0654.056.00.01.0
56995790227093950.720.01.0217.0731.018.2060831.00.00.0731.0217.00.01.0
5700579112713794.550.01.037.0505.021.4743241.00.00.0505.037.00.01.0